Mixed Logit Model Performance and Distributional Assumptions: Preferences and GM foods

Using mixed logit models to analyse choice data is common but requires ex ante specification of the functional forms of preference distributions. We make the case for greater use of bounded functional forms and propose the use of the Marginal Likelihood, calculated using Bayesian techniques, as a single measure of model performance across non nested mixed logit specifications. Using this measure leads to very different rankings of model specifications compared to alternative rule of thumb measures. The approach is illustrated using data from a choice experiment regarding GM food types which provides insights regarding the recent WTO dispute between the EU and the US, Canada and Argentina and whether labelling and trade regimes should be based on the production process or product composition. This paper draws work commissioned by UK Department of Environment Food and Rural Affairs (DEFRA). The views presented in this paper are those of the authors alone and should not be regarded as those of DEFRA or of individuals within DEFRA.     

Dynamic programming for valuing American options under a variance-gamma process

Lévy processes provide a solution to overcome the shortcomings of the lognormal hypothesis. A growing literature proposes the use of pure-jump Lévy processes, such as the variance-gamma (VG) model. In this setting, explicit solutions for derivative prices are unavailable, for instance, for the valuation of American options. We propose a dynamic programming approach coupled with finite elements for valuing American-style options under an extended VG model. Our numerical experiments confirm the convergence and show the efficiency of the proposed methodology. We also conduct a numerical investigation that focuses on American options on S&P 500 futures contracts.     

A Bayesian Nonparametric Investigation of the Predictive Effect of Exchange Rates on Commodity Prices

This study proposes a full Bayesian nonparametric procedure to investigate the predictive power of exchange rates in relation to commodity prices for three commodity-exporting countries: Canada, Australia, and New Zealand. We propose a new time-dependent infinite mixture of a normal linear regression model of the conditional distribution of the commodity price index. The mixing weights follow a set of Probit stick-breaking priors that are time-varying. We find that exchange rates have a positive predictive effect in general, but accounting for time variation does not improve forecasting performance. By contrast, the intercept in the regression and the lagged dependent variable show signs of parameter change over time in most cases, which is important in forecasting both the mean and the density of commodity prices one period ahead. The results also suggest that the variance is a large source of the time variation in the conditional distribution of commodity prices.     

A New Regime Switching Model with State–Varying Endogeneity

In this paper, we propose a state-varying endogenous regime switching model (the SERS model), which includes the endogenous regime switching model by Chang et al., the CCP model, as a special case. To estimate the unknown parameters in the SERS model, we propose a maximum likelihood estimation method. Monte Carlo simulation results show that in the absence of state-varying endogeneity, the SERS model and the CCP model perform similarly, while in the presence of state-varying endogeneity, the SERS model performs much better than the CCP model. Finally, we use the SERS model to analyze Chinese stock market returns, and our empirical results show that there exists strongly state-varying endogeneity in volatility switching for the Shanghai Composite Index returns. Moreover, the SERS model can indeed produce a much more realistic assessment for the regime switching process than the one obtained by the CCP model.     

Interest Rates and the Market for New Light Vehicles

We study the impact of interest rate changes on the demand and supply of new light vehicles in an environment where consumers and manufacturers face their own interest rates. Interest rate changes impact the auto market through both households and manufacturers. For the impact of rate changes on price and output growth, the household channel is quantitatively more important. A 100 basis‐point increase in both interest rates causes annual growth rates of production to fall from 1.0% to ?11.0% and sales to fall from 1.0% to ?2.9% in the short run.     

Toward an Archival Measure of the Likelihood of Auditor–Client Management Negotiation: An Exploration of the Audit Lag Measures Conjecture*

Salterio (2012) hypothesized that adaptations of an audit efficiency measure, audit report lag (the length of time between the financial statement year‐end date and the auditor's report date), could provide measures of underlying auditor–client management (ACM) negotiation likelihood. Salterio argued that these measures would enable archival researchers to examine issues that heretofore were the exclusive domain of experimental and field researchers. Using an audit report lag measure and a measure of abnormal audit report lag lags (the residual based on audit report lag determinants model), we show that a larger lag is associated with higher audit fees after controlling for other known determinants of audit fees. We also show that larger lags are associated with higher levels of discretionary accruals—that is, lower accrual quality. Based on our findings, we suggest that there is support for Salterio's hypothesis that audit report lags and abnormal audit report provide valid archival proxies for the differences in year‐end ACM negotiation likelihood. We suggest that this proxy will allow researchers to study issues related to published accounting numbers in light of whether negotiations are likely to have occurred in addition to providing regulators and others the means to determine what clients of audit firms are more likely to have different types of ACM relationships.     

Shrinkage estimation of the exponentiated Weibull regression model for time-to-event data

The exponentiated Weibull distribution is a convenient alternative to the generalized gamma distribution to model time-to-event data. It accommodates both monotone and nonmonotone hazard shapes, and flexible enough to describe data with wide ranging characteristics. It can also be used for regression analysis of time-to-event data. The maximum likelihood method is thus far the most widely used technique for inference, though there is a considerable body of research of improving the maximum likelihood estimators in terms of asymptotic efficiency. For example, there has recently been considerable attention on applying James–Stein shrinkage ideas to parameter estimation in regression models. We propose nonpenalty shrinkage estimation for the exponentiated Weibull regression model for time-to-event data. Comparative studies suggest that the shrinkage estimators outperform the maximum likelihood estimators in terms of statistical efficiency. Overall, the shrinkage method leads to more accurate statistical inference, a fundamental and desirable component of statistical theory.     

The effect of a Durbin–Watson pretest on confidence intervals in regression

Consider a linear regression model and suppose that our aim is to find a confidence interval for a specified linear combination of the regression parameters. In practice, it is common to perform a Durbin–Watson pretest of the null hypothesis of zero first‐order autocorrelation of the random errors against the alternative hypothesis of positive first‐order autocorrelation. If this null hypothesis is accepted then the confidence interval centered on the ordinary least squares estimator is used; otherwise the confidence interval centered on the feasible generalized least squares estimator is used. For any given design matrix and parameter of interest, we compare the confidence interval resulting from this two‐stage procedure and the confidence interval that is always centered on the feasible generalized least squares estimator, as follows. First, we compare the coverage probability functions of these confidence intervals. Second, we compute the scaled expected length of the confidence interval resulting from the two‐stage procedure, where the scaling is with respect to the expected length of the confidence interval centered on the feasible generalized least squares estimator, with the same minimum coverage probability. These comparisons are used to choose the better confidence interval, prior to any examination of the observed response vector.     

Route factors influencing trust and attitude toward TV shopping

Television (TV) shopping has notably changed the way people shop today. Due to the high uncertainty and risk associated with TV shopping transactions, trust is crucial to facilitating the adoption of TV shopping. Drawing upon the elaboration likelihood model (ELM), this study examined the effect of route factors on trust and attitude toward TV shopping. While media richness, host interaction, and price fairness act as central cues, ease of payment acts as a peripheral cue. This study employed structural equation modeling, and the sample consisted of 460 TV home shoppers in Taiwan. The results indicated that trust and attitude develop through a dual route that includes a central route and peripheral route. Trust is an important mediator influencing the relationship between route factors and attitude. Moreover, user experience moderates the relationships in the model. Therefore, this study advanced the previous knowledge of the ELM in the context of TV shopping.